Derivation of Constituent Problem Characteristics for the Application of Machine Learning Systems

Abstract

The increasing digitalization across all business sectors creates ever larger amounts of data. When analyzing this data to extract information, traditional data analysis methods easily meet their limits of performance. Conversely, methods from the machine learning (ML) spectrum promise to be a versatile tool for solving highly complex data-related tasks. Yet, organizations fail to identify relevant applications for ML due to a lack of systematic understanding of the applicability of the technology. This research paper draws upon the assumption that real-world problems exhibit distinctive characteristics that indicate their suitability for the application of ML. A framework for describing those constituent problem characteristics is proposed. Investigating the functional differences between traditional data analysis methods and ML, constituent characteristics are derived based on the distinctive technological abilities of ML. In order to differentiate simple ML methods from advanced ML methods regarding their technological abilities, a framework is presented. We suggest that advanced ML methods such as Deep Learning or Transfer Learning provide different potential benefits than simple methods such as Decision Trees, therefore necessitating this additional distinction.